Synthesis, structural characterization, and in vitro and in silico anticancer evaluation of chalcone-based heterocyclic and boron difluoride derivatives

In this research, a series of novel bioactive heterocyclic polar compounds were designed and synthesized by incorporating various functional groups to enhance their biological and functional properties. The work involved the synthesis of chalcone (1–9, 10–18), boron difluoride complex (19–21), pyrazoline (22–25), and pyrimidine (26–37) compounds with their structures characterized through spectral techniques including NMR (1H, 13C (APT), 1H–1H COSY), FT-IR spectroscopies, LC-MS spectrometry. The anticancer potential of all compounds was assessed using lung cancer cell lines (A549, Calu1, H1650), brain cancer cell lines (A172, C6, B35), as well as normal lung (Beas2B) and chondrocyte (HC) cell lines. Compounds exhibiting a GI50 value below 3.0 µg/mL and a TGI value under 100 µg/mL were classified as having anticancer activity, based on comparisons with the reference drug 5-fluorouracil (5FU). Overall, compounds 24, 34, and 36 emerged as the most promising lead candidates by combining potent antiproliferative activity, favorable GI50-derived selectivity indices, and low toxicity toward normal cells, whereas compounds 2, 32, and 35 demonstrated the highest overall biological potency across multiple cancer cell lines. Representative GI50 values as low as 1.0–1.2 µg/mL, TGI values as low as 1.3–2.5 µg/mL, and LC50 values generally exceeding 500 µg/mL in normal cell lines were obtained in the most responsive models, supporting the potential of these derivatives for further anticancer development. To support these detailed in vitro studies, the most active compounds targeting TP53, EGFR, Akt1, mTOR, ERK2, and caspase 3, which are effective in lung and brain cancer pathways, were interacted via in silico molecular docking. Through molecular docking, the interaction values of compounds 2, 6, 7, 8, 13, 14, 17, 18, 24, 27, 32, 35, 36, and 37 with the determined targets were calculated, and their potential as promising lead candidates for drug development was analyzed. © 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.